---
title: "MTCARS"
author: "Stefani Rmalho"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
theme: cerulean
source_code: embed
social: menu
---
```{r setup, include=FALSE}
### Fonte de pesquisa ##
# Documentacao do flexdashboard
# https://rmarkdown.rstudio.com/flexdashboard/index.html
### referencias ###
# https://r4ds.had.co.nz/
# Garrett Grolemund
# Hadley Wickham
# Carregando os modulos
library(ggplot2)
library(dplyr)
library(tibble)
library(ggcorrplot) # Plotar as correlacoes
library(DT) # Imprimir o dataset permitindo interacoes
library(psych) # Funcao describe
library(knitr) # Imprimir tabelas em um formato mais amigavel
library(modelr)
library(flexdashboard)
# Criando um dataset os dados mtcars
df <- datasets::mtcars %>%
rownames_to_column() %>%
as_tibble() %>%
mutate(vs = factor(vs),
am = factor(am),
gear = factor(gear),
carb = factor(carb),
cyl = factor(cyl))
# Tema dos graficos
theme_dahs <- theme_light() +
theme(panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.y = element_blank())
# Criando os modelos de regressao
model_lm_1 <- lm(mpg ~ wt + disp,df)
model_lm_2 <- lm(mpg ~ wt * disp,df)
```
Resultados {data-icon="fas fa-chart-line"}
=====================================
Row {data-height=20}
-----------------------------------------------------------------------
### Modelo A: mpg ~ wt + disp
### Modelo B: mpg ~ wt * disp
Row {data-height=130}
-----------------------------------------------------------------------
### R2
```{r}
# calculando o R2 do modelo 1
r2_1 <- round(rsquare(model_lm_1,df) * 100,2)
valueBox(r2_1,
icon = "fas fa-percent",
color = "primary")
```
### RMSE
```{r}
# calculando o RMSE do modelo 1
rmse_1 <- round(rmse(model_lm_1,df),4)
valueBox(rmse_1,
icon = "fas fa-chart-area",
color = "primary")
```
### R2
```{r}
# calculando o R2 do modelo 2
r2_2 <- round(rsquare(model_lm_2,df) * 100,2)
valueBox(r2_2,
icon = "fas fa-percent",
color = "primary")
```
### RMSE
```{r}
# calculando o RMSE do modelo 2
rmse_2 <- round(rmse(model_lm_2,df),4)
valueBox(rmse_2,
icon = "fas fa-chart-area",
color = "primary")
```
Row {data-height=500}
-----------------------------------------------------------------------
### Valores previstos x reais
```{r}
# Hitograma valores previstos x mpg
p5 <- df %>%
add_predictions(model_lm_1) %>%
ggplot() +
geom_histogram(aes(x = mpg, bins = 10), size = 1, fill = "sky blue") +
geom_histogram(aes(x = pred, bins = 10), size = 1, fill = "orange") +
theme_dahs
p5
```
### Frequência dos residuos
```{r}
# grafico com a frequencia dos residuos
p6 <- df %>%
add_residuals(model_lm_1) %>%
ggplot(aes(x = resid)) +
geom_freqpoly(bins = 40, color = "blue", size = 1) +
theme_dahs
p6
```
### Valores previstos x reais
```{r}
# Hitograma valores previstos x mpg
p7 <- df %>%
add_predictions(model_lm_2) %>%
ggplot() +
geom_histogram(aes(x = mpg, bins = 10), size = 1, fill = "sky blue") +
geom_histogram(aes(x = pred, bins = 10), size = 1, fill = "orange") +
theme_dahs
p7
```
### Frequência dos residuos
```{r}
# grafico com a frequencia dos residuos
p8 <- df %>%
add_residuals(model_lm_2) %>%
ggplot(aes(x = resid)) +
geom_freqpoly(bins = 40, color = "blue", size = 1) +
theme_dahs
p8
```
Row {data-height=350}
-----------------------------------------------------------------------
###
```{r}
# resudo do modelo
p9 <- summary(model_lm_1)[c(1,4)]
p9
```
###
```{r}
# resumo do modelo
p10 <- summary(model_lm_2)[c(1,4)]
p10
```
Analise Exploratória {data-icon="fa-signal"}
=====================================
Row {data-height=500}
-----------------------------------------------------------------------
### Distribuição de frequência por MPG
```{r}
# Plot p0 - Histograma do atributo mpg
p0 <- ggplot(df, aes(x = mpg)) +
geom_histogram(bins = 10, fill = "SKY blue") +
theme_dahs
p0
```
### Boxplot do atributo MPG por CYL
```{r}
# Plot p1 - Box plot mpg x cyl
p1 <- ggplot(df, aes(x = cyl, y = mpg)) +
geom_boxplot(fill = "orange", color = "orange3") +
theme_dahs
p1
```
### Relação entre o atributo MPG x WT
```{r}
# Plot p1 - scatterplot mpg x wt
p2 <- ggplot(df, aes(x = mpg, y = wt)) +
geom_point(size = 4, color = "red") +
geom_point(size = 3, color = "orange") +
geom_smooth(method = lm, se = FALSE, color = "grey2") +
theme_dahs
p2
```
### Análise de Correlação
```{r}
# Plot p3 - correlacoes
p3 <- df %>% select_if(is.numeric) %>%
cor() %>%
ggcorrplot(hc.order = TRUE,
type = "lower",
outline.color = "white")
p3
```
Row {data-height=500}
-----------------------------------------------------------------------
### Estatística Descritiva {.no-mobile}
```{r}
# plot p4 - tabela estatistica
p4 <- df %>%
describe() %>%
data.frame() %>%
rownames_to_column() %>%
rename(Atributo = rowname) %>%
select(-vars, -n) %>%
slice(-1) %>%
kable()
p4
```
Dataset {data-icon="fa-table"}
=====================================
### Dataset MTCARS {.no-mobile}
```{r}
# Tabela
t <- datatable(df)
t